Tongue FACS Notebook

Specify the tissue of interest, run the boilerplate code which sets up the functions and environment, load the tissue object.

tissue_of_interest = "Tongue"
library(here)
source(here("00_data_ingest", "02_tissue_analysis_rmd", "boilerplate.R"))

Loading required package: ggplot2
Loading required package: cowplot

Attaching package: ‘cowplot’

The following object is masked from ‘package:ggplot2’:

    ggsave

Loading required package: Matrix

Attaching package: ‘dplyr’

The following object is masked from ‘package:Biobase’:

    combine

The following objects are masked from ‘package:BiocGenerics’:

    combine, intersect, setdiff, union

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

tiss = load_tissue_facs(tissue_of_interest)

Performing log-normalization
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

[1] "Scaling data matrix"

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  |====================================================================================================| 100%

Calculating gene means
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|

Visualize top genes in principal components

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

PCElbowPlot(object = tiss)

Choose the number of principal components to use.

# Set number of principal components. 
n.pcs = 10

The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

# Set resolution 
res.used <- 0.7
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)

To visualize

# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 20, perplexity=30)
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)

Check expression of genes of interset.

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

How big are the clusters?

table(tiss@ident)

  0   1   2   3   4   5   6   7 
368 253 196 182 161 121 108  27 

tiss1=BuildClusterTree(tiss)

[1] "Finished averaging RNA for cluster 0"
[1] "Finished averaging RNA for cluster 1"
[1] "Finished averaging RNA for cluster 2"
[1] "Finished averaging RNA for cluster 3"
[1] "Finished averaging RNA for cluster 4"
[1] "Finished averaging RNA for cluster 5"
[1] "Finished averaging RNA for cluster 6"
[1] "Finished averaging RNA for cluster 7"

Which markers identify a specific cluster?

#cluster1.markers <- FindMarkers(object = tiss, ident.1 = 1, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster2.markers <- FindMarkers(object = tiss, ident.1 = 2, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster3.markers <- FindMarkers(object = tiss, ident.1 = 3, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster4.markers <- FindMarkers(object = tiss, ident.1 = 4, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster5.markers <- FindMarkers(object = tiss, ident.1 = 5, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster6.markers <- FindMarkers(object = tiss, ident.1 = 6, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
cluster7.markers <- FindMarkers(object = tiss, ident.1 = 6, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster0.markers <- FindMarkers(object = tiss, ident.1 = 0, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)

print(x = head(x = cluster6.markers, n = 30))

You can also compute all markers for all clusters at once. This may take some time.

tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)

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   |+++++++++++++++++++++++++++++++++++++             | 74% ~38s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~37s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~35s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~34s          
   |+++++++++++++++++++++++++++++++++++++++          | 78% ~32s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~31s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~30s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~28s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~27s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~25s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~24s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~22s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~21s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~19s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~18s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~16s          
   |+++++++++++++++++++++++++++++++++++++++++++++    | 90% ~15s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~13s          
   |++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~12s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 26s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~02m 06s      
   |++                                                | 2 % ~02m 00s      
   |++                                                | 3 % ~01m 58s      
   |+++                                               | 4 % ~01m 56s      
   |+++                                               | 5 % ~01m 55s      
   |++++                                              | 6 % ~01m 54s      
   |++++                                              | 7 % ~01m 53s      
   |+++++                                             | 8 % ~01m 51s      
   |+++++                                             | 9 % ~01m 51s      
   |++++++                                            | 10% ~01m 51s      
   |++++++                                            | 11% ~01m 49s      
   |+++++++                                           | 12% ~01m 48s      
   |+++++++                                           | 13% ~01m 46s      
   |++++++++                                          | 14% ~01m 45s      
   |++++++++                                          | 15% ~01m 44s      
   |+++++++++                                         | 16% ~01m 43s      
   |+++++++++                                         | 17% ~01m 42s      
   |++++++++++                                        | 18% ~01m 41s      
   |++++++++++                                        | 19% ~01m 40s      
   |+++++++++++                                       | 20% ~01m 39s      
   |+++++++++++                                       | 21% ~01m 38s      
   |++++++++++++                                      | 22% ~01m 37s      
   |++++++++++++                                      | 23% ~01m 35s      
   |+++++++++++++                                     | 24% ~01m 35s      
   |+++++++++++++                                     | 26% ~01m 34s      
   |++++++++++++++                                    | 27% ~01m 32s      
   |++++++++++++++                                    | 28% ~01m 32s      
   |+++++++++++++++                                   | 29% ~01m 30s      
   |+++++++++++++++                                   | 30% ~01m 30s      
   |++++++++++++++++                                  | 31% ~01m 29s      
   |++++++++++++++++                                  | 32% ~01m 28s      
   |+++++++++++++++++                                 | 33% ~01m 26s      
   |+++++++++++++++++                                 | 34% ~01m 25s      
   |++++++++++++++++++                                | 35% ~01m 24s      
   |++++++++++++++++++                                | 36% ~01m 22s      
   |+++++++++++++++++++                               | 37% ~01m 21s      
   |+++++++++++++++++++                               | 38% ~01m 20s      
   |++++++++++++++++++++                              | 39% ~01m 19s      
   |++++++++++++++++++++                              | 40% ~01m 18s      
   |+++++++++++++++++++++                             | 41% ~01m 16s      
   |+++++++++++++++++++++                             | 42% ~01m 15s      
   |++++++++++++++++++++++                            | 43% ~01m 14s      
   |++++++++++++++++++++++                            | 44% ~01m 12s      
   |+++++++++++++++++++++++                           | 45% ~01m 11s      
   |+++++++++++++++++++++++                           | 46% ~01m 10s      
   |++++++++++++++++++++++++                          | 47% ~01m 08s      
   |++++++++++++++++++++++++                          | 48% ~01m 07s      
   |+++++++++++++++++++++++++                         | 49% ~01m 06s      
   |+++++++++++++++++++++++++                         | 50% ~01m 05s      
   |++++++++++++++++++++++++++                        | 51% ~01m 03s      
   |+++++++++++++++++++++++++++                       | 52% ~01m 02s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 01s      
   |++++++++++++++++++++++++++++                      | 54% ~59s          
   |++++++++++++++++++++++++++++                      | 55% ~58s          
   |+++++++++++++++++++++++++++++                     | 56% ~57s          
   |+++++++++++++++++++++++++++++                     | 57% ~55s          
   |++++++++++++++++++++++++++++++                    | 58% ~54s          
   |++++++++++++++++++++++++++++++                    | 59% ~52s          
   |+++++++++++++++++++++++++++++++                   | 60% ~51s          
   |+++++++++++++++++++++++++++++++                   | 61% ~50s          
   |++++++++++++++++++++++++++++++++                  | 62% ~48s          
   |++++++++++++++++++++++++++++++++                  | 63% ~47s          
   |+++++++++++++++++++++++++++++++++                 | 64% ~46s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~44s          
   |++++++++++++++++++++++++++++++++++                | 66% ~43s          
   |++++++++++++++++++++++++++++++++++                | 67% ~42s          
   |+++++++++++++++++++++++++++++++++++               | 68% ~40s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~39s          
   |++++++++++++++++++++++++++++++++++++              | 70% ~38s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~36s          
   |+++++++++++++++++++++++++++++++++++++             | 72% ~35s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~34s          
   |++++++++++++++++++++++++++++++++++++++            | 74% ~33s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~31s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~30s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~29s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~27s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~26s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~25s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~23s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~22s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~21s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~19s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~18s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~17s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~16s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~14s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~13s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~12s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~09s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~07s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 07s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~03m 54s      
   |+                                                 | 2 % ~03m 51s      
   |++                                                | 3 % ~03m 47s      
   |++                                                | 4 % ~03m 44s      
   |+++                                               | 5 % ~03m 48s      
   |+++                                               | 6 % ~03m 46s      
   |++++                                              | 7 % ~03m 44s      
   |++++                                              | 8 % ~03m 42s      
   |+++++                                             | 9 % ~03m 40s      
   |+++++                                             | 10% ~03m 38s      
   |++++++                                            | 11% ~03m 35s      
   |++++++                                            | 12% ~03m 33s      
   |+++++++                                           | 13% ~03m 30s      
   |+++++++                                           | 14% ~03m 28s      
   |++++++++                                          | 15% ~03m 25s      
   |++++++++                                          | 16% ~03m 23s      
   |+++++++++                                         | 17% ~03m 20s      
   |+++++++++                                         | 18% ~03m 18s      
   |++++++++++                                        | 19% ~03m 16s      
   |++++++++++                                        | 20% ~03m 13s      
   |+++++++++++                                       | 21% ~03m 10s      
   |+++++++++++                                       | 22% ~03m 08s      
   |++++++++++++                                      | 23% ~03m 06s      
   |++++++++++++                                      | 24% ~03m 04s      
   |+++++++++++++                                     | 25% ~03m 01s      
   |+++++++++++++                                     | 26% ~02m 59s      
   |++++++++++++++                                    | 27% ~02m 56s      
   |++++++++++++++                                    | 28% ~02m 54s      
   |+++++++++++++++                                   | 29% ~02m 52s      
   |+++++++++++++++                                   | 30% ~02m 49s      
   |++++++++++++++++                                  | 31% ~02m 47s      
   |++++++++++++++++                                  | 32% ~02m 45s      
   |+++++++++++++++++                                 | 33% ~02m 42s      
   |+++++++++++++++++                                | 34% ~02m 39s      
   |++++++++++++++++++                                | 35% ~02m 38s      
   |++++++++++++++++++                                | 36% ~02m 35s      
   |+++++++++++++++++++                               | 37% ~02m 33s      
   |+++++++++++++++++++                               | 38% ~02m 31s      
   |++++++++++++++++++++                              | 39% ~02m 28s      
   |++++++++++++++++++++                              | 40% ~02m 26s      
   |+++++++++++++++++++++                             | 41% ~02m 24s      
   |+++++++++++++++++++++                             | 42% ~02m 21s      
   |++++++++++++++++++++++                            | 43% ~02m 19s      
   |++++++++++++++++++++++                            | 44% ~02m 17s      
   |+++++++++++++++++++++++                           | 45% ~02m 14s      
   |+++++++++++++++++++++++                           | 46% ~02m 12s      
   |++++++++++++++++++++++++                          | 47% ~02m 10s      
   |++++++++++++++++++++++++                          | 48% ~02m 10s      
   |+++++++++++++++++++++++++                         | 49% ~02m 10s      
   |+++++++++++++++++++++++++                         | 50% ~02m 07s      
   |++++++++++++++++++++++++++                        | 51% ~02m 04s      
   |++++++++++++++++++++++++++                        | 52% ~02m 01s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 59s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 56s      
   |++++++++++++++++++++++++++++                      | 55% ~01m 54s      
   |++++++++++++++++++++++++++++                     | 56% ~01m 51s      
   |+++++++++++++++++++++++++++++                     | 57% ~01m 49s      
   |+++++++++++++++++++++++++++++                     | 58% ~01m 46s      
   |++++++++++++++++++++++++++++++                    | 59% ~01m 44s      
   |++++++++++++++++++++++++++++++                    | 60% ~01m 41s      
   |+++++++++++++++++++++++++++++++                   | 61% ~01m 38s      
   |+++++++++++++++++++++++++++++++                   | 62% ~01m 36s      
   |++++++++++++++++++++++++++++++++                  | 63% ~01m 33s      
   |++++++++++++++++++++++++++++++++                  | 64% ~01m 31s      
   |+++++++++++++++++++++++++++++++++                 | 65% ~01m 28s      
   |+++++++++++++++++++++++++++++++++                 | 66% ~01m 25s      
   |++++++++++++++++++++++++++++++++++                | 67% ~01m 23s      
   |++++++++++++++++++++++++++++++++++               | 68% ~01m 20s      
   |+++++++++++++++++++++++++++++++++++               | 69% ~01m 18s      
   |+++++++++++++++++++++++++++++++++++               | 70% ~01m 16s      
   |++++++++++++++++++++++++++++++++++++              | 71% ~01m 13s      
   |++++++++++++++++++++++++++++++++++++              | 72% ~01m 10s      
   |+++++++++++++++++++++++++++++++++++++             | 73% ~01m 08s      
   |+++++++++++++++++++++++++++++++++++++             | 74% ~01m 05s      
   |++++++++++++++++++++++++++++++++++++++            | 75% ~01m 03s      
   |++++++++++++++++++++++++++++++++++++++            | 76% ~01m 00s      
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~58s          
   |+++++++++++++++++++++++++++++++++++++++          | 78% ~55s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~53s          
   |++++++++++++++++++++++++++++++++++++++++         | 80% ~50s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~48s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~45s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~43s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~40s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~38s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~35s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~33s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~30s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~28s          
   |+++++++++++++++++++++++++++++++++++++++++++++    | 90% ~25s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~23s          
   |++++++++++++++++++++++++++++++++++++++++++++++   | 92% ~20s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~18s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~15s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~13s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~08s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 04m 09s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~02m 39s      
   |++                                                | 2 % ~02m 36s      
   |++                                                | 3 % ~02m 34s      
   |+++                                               | 4 % ~02m 29s      
   |+++                                               | 5 % ~02m 25s      
   |++++                                              | 6 % ~02m 21s      
   |++++                                              | 7 % ~02m 22s      
   |+++++                                             | 8 % ~02m 21s      
   |+++++                                             | 9 % ~02m 19s      
   |++++++                                            | 10% ~02m 17s      
   |++++++                                            | 11% ~02m 17s      
   |+++++++                                           | 12% ~02m 15s      
   |+++++++                                           | 13% ~02m 13s      
   |++++++++                                          | 14% ~02m 14s      
   |++++++++                                          | 15% ~02m 22s      
   |+++++++++                                         | 16% ~02m 22s      
   |+++++++++                                         | 17% ~02m 20s      
   |++++++++++                                        | 18% ~02m 19s      
   |++++++++++                                        | 19% ~02m 16s      
   |+++++++++++                                       | 20% ~02m 14s      
   |+++++++++++                                       | 21% ~02m 11s      
   |++++++++++++                                      | 22% ~02m 09s      
   |++++++++++++                                      | 23% ~02m 07s      
   |+++++++++++++                                     | 24% ~02m 04s      
   |+++++++++++++                                     | 25% ~02m 02s      
   |++++++++++++++                                    | 26% ~01m 60s      
   |++++++++++++++                                    | 27% ~01m 58s      
   |+++++++++++++++                                   | 28% ~01m 56s      
   |+++++++++++++++                                   | 29% ~01m 53s      
   |++++++++++++++++                                  | 30% ~01m 51s      
   |++++++++++++++++                                  | 31% ~01m 49s      
   |+++++++++++++++++                                 | 32% ~01m 47s      
   |+++++++++++++++++                                 | 33% ~01m 45s      
   |++++++++++++++++++                                | 34% ~01m 44s      
   |++++++++++++++++++                                | 35% ~01m 42s      
   |+++++++++++++++++++                               | 36% ~01m 40s      
   |+++++++++++++++++++                               | 37% ~01m 38s      
   |++++++++++++++++++++                              | 38% ~01m 37s      
   |++++++++++++++++++++                              | 39% ~01m 36s      
   |+++++++++++++++++++++                             | 40% ~01m 34s      
   |+++++++++++++++++++++                             | 41% ~01m 32s      
   |++++++++++++++++++++++                            | 42% ~01m 31s      
   |++++++++++++++++++++++                            | 43% ~01m 29s      
   |+++++++++++++++++++++++                           | 44% ~01m 28s      
   |+++++++++++++++++++++++                           | 45% ~01m 26s      
   |++++++++++++++++++++++++                          | 46% ~01m 25s      
   |++++++++++++++++++++++++                          | 47% ~01m 23s      
   |+++++++++++++++++++++++++                         | 48% ~01m 21s      
   |+++++++++++++++++++++++++                         | 49% ~01m 19s      
   |++++++++++++++++++++++++++                        | 51% ~01m 17s      
   |++++++++++++++++++++++++++                        | 52% ~01m 16s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 14s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 12s      
   |++++++++++++++++++++++++++++                      | 55% ~01m 11s      
   |++++++++++++++++++++++++++++                      | 56% ~01m 09s      
   |+++++++++++++++++++++++++++++                     | 57% ~01m 07s      
   |+++++++++++++++++++++++++++++                     | 58% ~01m 06s      
   |++++++++++++++++++++++++++++++                    | 59% ~01m 04s      
   |++++++++++++++++++++++++++++++                    | 60% ~01m 03s      
   |+++++++++++++++++++++++++++++++                   | 61% ~01m 01s      
   |+++++++++++++++++++++++++++++++                   | 62% ~59s          
   |++++++++++++++++++++++++++++++++                  | 63% ~58s          
   |++++++++++++++++++++++++++++++++                  | 64% ~56s          
   |+++++++++++++++++++++++++++++++++                 | 65% ~54s          
   |+++++++++++++++++++++++++++++++++                 | 66% ~53s          
   |++++++++++++++++++++++++++++++++++                | 67% ~51s          
   |++++++++++++++++++++++++++++++++++                | 68% ~50s          
   |+++++++++++++++++++++++++++++++++++               | 69% ~48s          
   |+++++++++++++++++++++++++++++++++++               | 70% ~47s          
   |++++++++++++++++++++++++++++++++++++              | 71% ~45s          
   |++++++++++++++++++++++++++++++++++++              | 72% ~43s          
   |+++++++++++++++++++++++++++++++++++++             | 73% ~42s          
   |+++++++++++++++++++++++++++++++++++++             | 74% ~40s          
   |++++++++++++++++++++++++++++++++++++++            | 75% ~39s          
   |++++++++++++++++++++++++++++++++++++++            | 76% ~37s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~35s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~34s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~32s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~31s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~29s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~28s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~26s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~25s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~23s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~22s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~20s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~19s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~17s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~16s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~14s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~12s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~11s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~09s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~08s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~06s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~05s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 35s

   |                                                  | 0 % ~calculating  
   |+                                                 | 1 % ~04m 18s      
   |++                                                | 2 % ~04m 59s      
   |++                                                | 3 % ~04m 57s      
   |+++                                               | 4 % ~04m 41s      
   |+++                                               | 5 % ~04m 25s      
   |++++                                              | 6 % ~04m 13s      
   |++++                                              | 7 % ~04m 03s      
   |+++++                                             | 8 % ~03m 57s      
   |+++++                                             | 9 % ~03m 53s      
   |++++++                                            | 10% ~03m 47s      
   |++++++                                            | 11% ~03m 44s      
   |+++++++                                           | 12% ~03m 39s      
   |+++++++                                           | 13% ~03m 37s      
   |++++++++                                          | 14% ~03m 35s      
   |++++++++                                          | 15% ~03m 30s      
   |+++++++++                                         | 16% ~03m 26s      
   |+++++++++                                         | 17% ~03m 23s      
   |++++++++++                                        | 18% ~03m 25s      
   |++++++++++                                        | 19% ~03m 26s      
   |+++++++++++                                       | 20% ~03m 27s      
   |+++++++++++                                       | 21% ~03m 24s      
   |++++++++++++                                      | 22% ~03m 21s      
   |++++++++++++                                      | 23% ~03m 17s      
   |+++++++++++++                                     | 24% ~03m 14s      
   |+++++++++++++                                     | 25% ~03m 11s      
   |++++++++++++++                                    | 26% ~03m 07s      
   |++++++++++++++                                    | 27% ~03m 04s      
   |+++++++++++++++                                   | 28% ~03m 01s      
   |+++++++++++++++                                   | 29% ~02m 57s      
   |++++++++++++++++                                  | 30% ~02m 54s      
   |++++++++++++++++                                  | 31% ~02m 51s      
   |+++++++++++++++++                                 | 32% ~02m 48s      
   |+++++++++++++++++                                 | 33% ~02m 45s      
   |++++++++++++++++++                                | 34% ~02m 43s      
   |++++++++++++++++++                                | 35% ~02m 40s      
   |+++++++++++++++++++                               | 36% ~02m 37s      
   |+++++++++++++++++++                               | 37% ~02m 34s      
   |++++++++++++++++++++                              | 38% ~02m 31s      
   |++++++++++++++++++++                              | 39% ~02m 29s      
   |+++++++++++++++++++++                             | 40% ~02m 26s      
   |+++++++++++++++++++++                             | 41% ~02m 23s      
   |++++++++++++++++++++++                            | 42% ~02m 21s      
   |++++++++++++++++++++++                            | 43% ~02m 18s      
   |+++++++++++++++++++++++                           | 44% ~02m 16s      
   |+++++++++++++++++++++++                           | 45% ~02m 13s      
   |++++++++++++++++++++++++                          | 46% ~02m 11s      
   |++++++++++++++++++++++++                          | 47% ~02m 08s      
   |+++++++++++++++++++++++++                         | 48% ~02m 05s      
   |+++++++++++++++++++++++++                         | 49% ~02m 03s      
   |++++++++++++++++++++++++++                        | 51% ~02m 00s      
   |++++++++++++++++++++++++++                        | 52% ~01m 59s      
   |+++++++++++++++++++++++++++                       | 53% ~01m 56s      
   |+++++++++++++++++++++++++++                       | 54% ~01m 54s      
   |++++++++++++++++++++++++++++                      | 55% ~01m 52s      
   |++++++++++++++++++++++++++++                      | 56% ~01m 49s      
   |+++++++++++++++++++++++++++++                     | 57% ~01m 47s      
   |+++++++++++++++++++++++++++++                     | 58% ~01m 44s      
   |++++++++++++++++++++++++++++++                    | 59% ~01m 42s      
   |++++++++++++++++++++++++++++++                    | 60% ~01m 39s      
   |+++++++++++++++++++++++++++++++                   | 61% ~01m 37s      
   |+++++++++++++++++++++++++++++++                   | 62% ~01m 34s      
   |++++++++++++++++++++++++++++++++                  | 63% ~01m 31s      
   |++++++++++++++++++++++++++++++++                  | 64% ~01m 29s      
   |+++++++++++++++++++++++++++++++++                 | 65% ~01m 26s      
   |+++++++++++++++++++++++++++++++++                 | 66% ~01m 24s      
   |++++++++++++++++++++++++++++++++++                | 67% ~01m 21s      
   |++++++++++++++++++++++++++++++++++                | 68% ~01m 19s      
   |+++++++++++++++++++++++++++++++++++               | 69% ~01m 16s      
   |+++++++++++++++++++++++++++++++++++               | 70% ~01m 14s      
   |++++++++++++++++++++++++++++++++++++              | 71% ~01m 11s      
   |++++++++++++++++++++++++++++++++++++              | 72% ~01m 09s      
   |+++++++++++++++++++++++++++++++++++++             | 73% ~01m 06s      
   |+++++++++++++++++++++++++++++++++++++             | 74% ~01m 04s      
   |++++++++++++++++++++++++++++++++++++++            | 75% ~01m 02s      
   |++++++++++++++++++++++++++++++++++++++            | 76% ~59s          
   |+++++++++++++++++++++++++++++++++++++++           | 77% ~57s          
   |+++++++++++++++++++++++++++++++++++++++           | 78% ~54s          
   |++++++++++++++++++++++++++++++++++++++++          | 79% ~52s          
   |++++++++++++++++++++++++++++++++++++++++          | 80% ~49s          
   |+++++++++++++++++++++++++++++++++++++++++         | 81% ~47s          
   |+++++++++++++++++++++++++++++++++++++++++         | 82% ~44s          
   |++++++++++++++++++++++++++++++++++++++++++        | 83% ~42s          
   |++++++++++++++++++++++++++++++++++++++++++        | 84% ~39s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~37s          
   |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~34s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~32s          
   |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~30s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~27s          
   |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~25s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~22s          
   |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~20s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~17s          
   |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~15s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~12s          
   |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~10s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~07s          
   |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~05s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s          
   |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 04m 04s

Display the top markers you computed above.

tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)

Generate an expression heatmap for given cells and genes. In this case, we are plotting the top 20 markers (or all markers if less than 20) for each cluster.

Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:

0: basal cell of epidermis (CL:0002187) 1: basal cell of epidermis (CL:0002187) 2: proliferating basal cell of epidermis (CL:0002187) 3: differentiated keratinocytes (CL:0000312) 4: differentiating basal cell of epidermis (CL:0002187) 5: suprabasal differentiating keratinocytes (CL:0000312) 6: basal cell of epidermis (CL:0002187) 7: filiform differentiated keratinocytes (CL:0000312)

# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7)
free_annotation <- c(
"basal cells", 
"basal cells", 
"proliferating", 
"differentiated keratinocyte", 
"differentiating basal cells", 
"suprabasal differentiating keratinocytes", 
"basal cells",
"filiform differentiated keratinocytes" 
)
cell_ontology_class <- c(
  "basal cell of epidermis", 
  "basal cell of epidermis", 
  "basal cell of epidermis", 
  "keratinocyte",
  "basal cell of epidermis",
  "keratinocyte",
  "basal cell of epidermis",
  "keratinocyte"
)
cell_ontology_id <- c(
  "CL:0002187",
  "CL:0002187", 
  "CL:0002187",
  "CL:0000312",
  "CL:0002187",
  "CL:0000312",
  "CL:0002187",
  "CL:0000312"
)
tiss@meta.data[,'free_annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = free_annotation)
tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class')

Supplementary Figures

Fig. suppTongue1. tSNE based on free annotation

Fig. suppTongue2. Expression of genes used for annotation of the clusters. Feature plot from both platforms on: Top2a, Cdc20, Mki67, Krt15, Krt5, Krt14, Sbsn, Krt10, Krt4, Krt13, Krt36, Krt84, Hoxc13

Fig. suppTongue3. Expression of genes known to be expressed in taste receptor cells. Violin plot from both platforms on: Krt8, Tas1r1, Tas1r2, Tas1r3, Ncam1, Car4

Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.

TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.sex")

Print a table showing the count of cells in each identity category from each plate.

table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))

   
    B002777 B002780 D041894 D041904 D041911
  0      61      58      90      79      80
  1      46      12      67      56      72
  2      85      24      28      45      14
  3      40      19      38      46      39
  4      20       8      44      48      41
  5      10       5      35      34      37
  6       1       7      29      14      57
  7       3       3       7      11       3

Save the Robject for later

When you save the annotated tissue, please give it a name.

filename = here('00_data_ingest', '04_tissue_robj_generated', 
                     paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)

[1] "/Users/Jintonic/Documents/GitHub/tabula-muris/00_data_ingest/04_tissue_robj_generated/facsTongue_seurat_tiss.Robj"

save(tiss, file=filename)

# To reload a saved object
filename = here('00_data_ingest', '04_tissue_robj_generated', 
                      paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
load(file=filename)

Export the final metadata

So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.

head(tiss@meta.data)

filename = here('00_data_ingest', '03_tissue_annotation_csv', 
          paste0(tissue_of_interest, "_facs_cell_ontology_class.csv"))
write.csv(FetchData(tiss, c('plate.barcode','cell_ontology_class','cell_ontology_id', 'free_annotation', 'tSNE_1', 'tSNE_2')), file=filename)